Employee Performance Prediction Using Machine

Learning
Dr. Murthy D H R Manoj G
Associate professor, CS&E Computer Science and Engineering
R.L.Jalappa Institute of Technology R.L.Jalappa institute of Technology
Doddaballapur, Karanataka, India Doddaballapur, Karanataka,India
murthydhr@rljit.in manoj00in7@gmail.com

Manodarshan K Shashank T S
Computer Science and Engineering Computer Science and Engineering
R.L.Jalappa institute of Technology R.L.Jalappa institute of Technology
Doddaballapur, Karanataka, India Doddaballapur, Karanataka,India
manukro369@gmail.com shashankts24@gmail.com

Abstract—In the rapidly evolving digital workplace, optimizing Machine learning (ML), particularly supervised classification
employee performance has become a top priority for models, provides an opportunity to modernize performance
organizations. Traditional performance appraisal methods often evaluation. By training algorithms on historical employee
fall short due to human bias, inconsistency, and lack of real-time data—such as demographics, performance scores, job
adaptability. This paper presents a data-driven solution to
predict employee performance using supervised machine
satisfaction, and professional history—ML systems can learn
learning techniques. We propose a model that leverages to classify employees into risk or excellence categories,
employee-specific attributes such as education, department, enabling HR to intervene with data-backed strategies.
years of experience, performance history, satisfaction levels, and
more to classify employees into performance categories: high, This paper presents an intelligent performance prediction
medium, and low. We explore the implementation of algorithms system using Random Forest and SVM classifiers integrated
such as Decision Trees, Random Forest, and Support Vector into a web-based dashboard. It helps automate employee
Machines (SVM), and assess their predictive accuracy. A web- assessment, support performance interventions, and improve
based application, built using Streamlit, offers HR departments retention by identifying potential early.
an intuitive interface for performance forecasting and
comparative analysis. Experimental results validate the system’s
reliability and usability. The proposed system has significant
II. LITERATURE SURVEY
implications for strategic HR planning, talent management, and
productivity optimization.
Several studies and systems have been developed in recent
years to address gesture and sign language recognition using
Keywords—Employee Performance Prediction, Supervised artificial intelligence and computer vision. Traditional sign
Machine Learning, Decision Trees, Random Forest, Support language recognition approaches have largely focused on
Vector Machines (SVM), Performance Appraisal, Data-Driven static gestures or alphabet-based detection, limiting their
Model, HR Analytics, Employee Attributes, Performance capacity for dynamic, real-time interactions.
Categories, Streamlit Application, Predictive Accuracy, Talent
Management, Strategic HR Planning, Productivity Optimization. In [1], a CNN-based hand gesture recognition system was
introduced to classify static signs from image datasets.
I. INTRODUCTION However, this system was constrained to single-frame
Employee performance directly influences organizational analysis and lacked the temporal awareness necessary for
productivity, profitability, and culture. High-performing continuous gesture understanding.
employees contribute disproportionately to growth and
innovation, whereas underperforming individuals may hinder To improve accuracy, [2] proposed a glove-based motion
progress, morale, and service quality. Given the strategic tracking system for sign language recognition. While it
importance of talent management, accurate, unbiased, and demonstrated high precision, its reliance on wearable sensors
scalable performance evaluation tools are essential. reduced practicality and accessibility for general users. A
Traditionally, performance reviews have relied on managerial more accessible alternative was explored in [3], which used
observations, peer feedback, and KPI tracking. While webcam input and OpenCV for recognizing ASL alphabets.
valuable, these methods are susceptible to subjectivity and Though promising, this system could not handle sentence-
lack real-time decision support. This makes it difficult for HR level recognition or overlapping gestures, and it did not
professionals to identify patterns, provide timely include live audio output.
interventions, and plan effectively.
 • 4. Model Training: Training and validation
In [4], a deep learning approach using Long Short-Term
Memory (LSTM) networks was developed to recognize using multiple classifiers. Random Forest
sequences of gestures in sign language videos. This method achieved the best results.
significantly improved dynamic gesture recognition but • 5. Deployment: An interactive Streamlit app for
demanded large labeled datasets and high computational prediction, comparison, and Excel-based data
resources. MediaPipe was applied in [5] for gesture-based tracking.
keypoint detection, yet its implementation was limited to
isolated hand tracking and failed to leverage full-body This approach ensures both model performance and
landmarks for better contextual understanding. accessibility for non-technical users.
Further efforts in [6] introduced a mobile application for sign IV. SYSTEM ARCHITECTURE
recognition, though it lacked real-time feedback and speech
synthesis, reducing its effectiveness as a communication tool.
A multimodal system integrating gesture and facial cues was The architecture of the proposed employee performance
presented in [7], yet the absence of an integrated, user- evaluation system is designed as a modular pipeline that
friendly interface limited its practical usability. More recent supports data collection, processing, prediction, and
works, such as [8] and [9], attempted to enhance gesture deployment. It ensures scalability, usability, and accuracy in
classification accuracy by combining pose estimation tools classifying employee performance levels. The architectural
with LSTM or GRU-based sequence models. Nevertheless, flow includes the following components:
these systems frequently lacked intuitive user interfaces and
speech output functionality. 1. Data Ingestion and Preprocessing:

Finally, [10] highlighted the importance of audio-visual Raw HR data is collected from both primary sources
feedback to engage users more effectively. However, (e.g., employee surveys) and secondary sources
challenges such as latency and large model sizes hindered (e.g., HR databases).
real-time performance and smooth deployment. Despite these
advancements, most existing systems fall short in delivering Preprocessing steps include:
a comprehensive solution that includes real-time gesture
detection, full-body landmark tracking, responsive visual
feedback, and audio synthesis. This paper addresses these • Handling Missing Data through
limitations by proposing a system that integrates MediaPipe imputation.
Holistic for complete keypoint detection, LSTM networks for • Normalization and
temporal classification, and speech synthesis in a user- Standardization of numerical
friendly interface—enabling seamless and accessible features.
communication for the deaf and mute community. • Label Encoding of categorical
variables for model compatibility.

2. Feature Extraction:
III. PROPOSED SYSTEM
Key performance-related features are selected,
The system predicts employee performance by classifying including KPIs, attendance, promotion history, and
employees into high, medium, or low performers using training completion rates.Dimensionality
supervised learning. It uses HR attributes such as: Reduction techniques like PCA are applied to retain
only the most relevant attributes and reduce
• Job satisfaction computational load.
• Last salary hike percentage
• Years of experience 3. Model Building:
• Number of companies worked
• Work environment feedback The system uses supervised machine learning
• Department and role algorithms such as Decision Trees, Random
• Promotion history Forest, and SVM.Data is split into training and
testing sets to ensure generalization and prevent
The system’s workflow includes: overfitting.

• 1. Data Collection: Raw HR datasets from internal 4. Model Training and Evaluation
databases or Excel inputs.
• 2. Preprocessing: Imputation of missing values, Cleaned data is fed into the selected models.Models
label encoding for categorical data, and scaling of are evaluated using metrics such as accuracy,
numerical fields. precision, recall, and F1-score.
• 3. Feature Selection: Filtering of high-variance or
correlated features using correlation matrices and 5. Hyperparameter Tuning:
PCA.
Performance is improved using tuning methods
like:Grid Search or Random Search.Cross-
Validation to validate model robustness and prevent
overfitting.
 6. Deployment: V. RESULTS AND DISCUSSION

The trained model is deployed via a Streamlit-based After training the Random Forest model on real HR data, we
web application.Key deployment features include: achieved:
• Accuracy: 91.2%
• Real-Time Prediction Interface for HR • Precision : 88.5%
users. • Recall : 90.1%
• F1 Score: 89.3%
• Interactive Dashboards to visualize
employee performance trends.
Feature Importance :
• Excel File Integration for storing 1. Job Satisfaction
predictions and comparisons.Comparison 2. Last Salary Hike Percentage
Module to assess current versus previous 3. Number of Companies Worked
performance data. 4. Job Role
5. Department

The Streamlit interface was tested by a group of HR managers

who reported improved clarity in decision-making and reduced
time in preparing evaluation reports. The Excel export feature
enabled easy integration with current reporting formats.

SAMPLE OUTPUT

Fig. 1. Methodology

Fig. 1. Streamlit Web Application Interface

IV. IMPLEMENTATION
This screenshot shows the main interface of the employee
Technologies Used: performance prediction application, where users can upload
datasets and preview employee records.
• Python 3.10
• Scikit-learn for model development
• Pandas and NumPy for data handling
• Streamlit for app development
• Matplotlib for visual analytics

Classifier Configuration:

python
RandomForestClassifier(
n_estimators=200,
max_depth=10,
class_weight='balanced',
random_state=42
)

Workflow:
• HR uploads Excel sheet.
• Model is trained in real-time or preloaded.
• Employee attributes are entered via dropdown
selectors. Fig. 2. User Input Form in Streamlit
• Prediction is shown instantly with probability scores.
• Output is saved to Excel for future comparison. This figure displays the form through which users input or
select employee details for performance prediction
The app also includes a “Compare Performance” feature,
where past and present evaluations for the same employee ID
can be viewed side-by-side.
 FUTURE SCOPE

The current study demonstrates the effectiveness of machine

learning models in predicting employee performance based on
structured HR data. However, several opportunities exist to
extend this research and enhance its real-world applicability:

1. Integration of Unstructured Data:

Fig. 3. Predicted Employee Performance
Future implementations can incorporate unstructured
The screenshot illustrates the output section after submitting data sources such as email sentiment, peer feedback,
inputs, showing the predicted performance category (e.g., or performance reviews using natural language
increase, decrease, neutral). processing (NLP). This would provide a more
comprehensive evaluation of employee behavior and
engagement.

2. Real-Time Prediction Models:

Integrating real-time data streams from HR

management systems and productivity tools (e.g.,
Jira, Slack, or Microsoft Teams) could allow for
continuous performance monitoring and dynamic
decision-making.

3. Explainable AI (XAI):

Incorporating explainable AI techniques will enhance

model transparency and foster trust among HR
professionals and employees by providing clear
justifications for each prediction.
Fig. 4. Predicted Employee Performanc
4. Cross-Organization Model Generalization:
This figure demonstrates how users can compare current
predictions with previous performance records using an Future work could focus on testing model
employee number. generalizability across different industries,
organizational sizes, and geographies, thereby
enhancing its adaptability and robustness.

5. Ethical and Fairness Audits:

As predictive systems gain influence over HR

decisions, future research should focus on fairness-
aware modeling techniques that actively mitigate
algorithmic bias and support equitable workplace
practices
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